class_docstr: Base class for heterogeneous ensemble of learners.
  
  Parameters
  ----------
  estimators : list of (str, estimator) tuples
      The ensemble of estimators to use in the ensemble. Each element of the
      list is defined as a tuple of string (i.e. name of the estimator) and
      an estimator instance. An estimator can be set to `'drop'` using
      `set_params`.
  
  Attributes
  ----------
  estimators_ : list of estimators
      The elements of the estimators parameter, having been fitted on the
      training data. If an estimator has been set to `'drop'`, it will not
      appear in `estimators_`.
docstr: Set the parameters of an estimator from the ensemble.
  
  Valid parameter keys can be listed with `get_params()`. Note that you
  can directly set the parameters of the estimators contained in
  `estimators`.
  
  Parameters
  ----------
  **params : keyword arguments
      Specific parameters using e.g.
      `set_params(parameter_name=new_value)`. In addition, to setting the
      parameters of the estimator, the individual estimator of the
      estimators can also be set, or can be removed by setting them to
      'drop'.
  
  Returns
  -------
  self : object
      Estimator instance.
docstr_tok: ["Set","the","parameters","of","an","estimator","from","the","ensemble",".","Valid","parameter","keys","can","be","listed","with","`","get_params","(",")","`",".","Note","that","you","can","directly","set","the","parameters","of","the","estimators","contained","in","`","estimators","`",".","Parameters","--","--","--","--","--","*","*","params",":","keyword","arguments","Specific","parameters","using","e.g",".","`","set_params","(","parameter_name=new_value",")","`",".","In","addition",",","to","setting","the","parameters","of","the","estimator",",","the","individual","estimator","of","the","estimators","can","also","be","set",",","or","can","be","removed","by","setting","them","to","'drop","'",".","Returns","--","--","--","-","self",":","object","Estimator","instance","."]
code: def set_params(self, **params):
      """
      Set the parameters of an estimator from the ensemble.
  
      Valid parameter keys can be listed with `get_params()`. Note that you
      can directly set the parameters of the estimators contained in
      `estimators`.
  
      Parameters
      ----------
      **params : keyword arguments
          Specific parameters using e.g.
          `set_params(parameter_name=new_value)`. In addition, to setting the
          parameters of the estimator, the individual estimator of the
          estimators can also be set, or can be removed by setting them to
          'drop'.
  
      Returns
      -------
      self : object
          Estimator instance.
      """
      super()._set_params("estimators", **params)
      return self
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from typing import List
  import numpy
  from joblib import effective_n_jobs
  from ..base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier, is_regressor
  from ..utils import Bunch, _print_elapsed_time, check_random_state, deprecated
  from ..utils.metaestimators import _BaseComposition
label_desc: Use image node_id 4 for calling the _BaseHeterogeneousEnsemble obj's underlying member method code with example usage: obj.set_params() and returns: self